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CU Committee Report: Generative Artificial Intelligence for Education and Pedagogy | Center for Teaching Innovation

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Chairs: Kavita Bala, Alex ColvinCommittee members: Morten H. Christiansen, Allison Weiner Heinemann, Sarah Kreps, Lionel Levine, Christina Liang, David Mimno, Sasha Rush, Deirdre Snyder, Wendy E. Tarlow, Felix Thoemmes, Rob Vanderlan, Andrea Stevenson Won, Alan Zehnder, Malte Ziewitz

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TeachAI brings together education leaders and technology experts to assist governments and education authorities in teaching with and about AI. The initiative is led by Code.org, ETS, the International Society for Technology in Education, Khan Academy, and the World Economic Forum and advised by a diverse group of 100+ organizations, governments, and individuals. TeachAI’s goals include guiding policy, building community and capacity, and increasing awareness. To learn more about TeachAI, please visit www.teachai.org/about.

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K-12 Generative AI Readiness Checklist October 11 2023 V1.1.pdf

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Mohammed Swafwan and Ahmed Abdelsalam 9-B1

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Mohammed Swafwan and Ahmed Abdelsalam 9-B1

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1_Every AI must know it's limits
2_Try to show or express feelings to support others
3_Should be kind and helpful

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20 Ways to use ChatGPT in the Classroom 

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20 Ways to use ChatGPT in the Classroom 

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From a a great article at Ditch that Textbook, about halfway down (with descriptions for each number)
https://ditchthattextbook.com/ai/

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A Great chart from Ditch the Textbook on how to approach Plagiarism and Cheating in the age of ChatGPT

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The AI and Education Policy Exchange was the first of CIRCLS' AI Expertise Exchanges, running from March-June, 2021. The exchange took place bi-weekly over 7 sessions on Zoom, with a total involvement of 49 total community members. Participants represented multiple stakeholders including faculty members, graduate students, industry experts, K-12 teachers, and district representatives. The goals of the exchange were to: 1) Promote learning through a Speaker Series that brought community members together to share their knowledge and expertise related to AI and Education Policy, 2) Synthesize ideas to develop Community Resources surrounding AI and Education Policy issues, and 3) Effect change by converging on a set of Policy Initiatives to be pursued in smaller groups by Exchange Members.

CURRENT OPPORTUNITY: ISLS 2022 Workshop on AI and educational policy
The CIRCLS team is excited to be organizing a pre-conference workshop on AI and educational policy at the International Society for the Learning Sciences Annual Meeting in 2022. If you are attending ISLS and are interested in participating, please visit our event posting to learn more and apply.

Policy Initiatives
In its final sessions, the exchange split into three small groups to create policy briefs and deliverables. Each initiative links to a policy document:

1. Initiative One: AI in EdTech Vendor Pledge

2. Initiative Two: Policy Brief on Federal Guidelines for AI in EdTech

3. Initiative Three: School Policies for Integrating AI in Classroom Practices

If you're interested in plugging into any of these initiatives or learning more, please reach out to us! We welcome any feedback and input that our community has to offer.

Community Resources
The exchange has led to two main community resources:

1. AI and Education Policy Reading List: The AI and Education Policy Reading List is an annotated compilation of websites, primers, reports, academic articles, and databases that were shared by Exchange members over the course of our sessions. The reading list focuses on the intersections of policy, AI, and education in global and national settings, and provides additional starting points for learning about AI privacy and AI resources for educators.

2. Identification of Priority Areas in AI and Ed Policy: As a result of the discussions surrounding the Speaker Sessions, members identified four high priority concerns in AI and Education Policy: transparency and disclosure, communication and user agency, AI literacy, and ethics and equity. Members created a taxonomy of potential actions to take surrounding those concerns.

All videos of exchange meetings, slide decks, and activities are available to Exchange Members. If you are interested in being added to this folder, please contact us.

Speaker Series
The AI and Ed Policy Expertise Exchange hosted four speakers sessions on global, national, state-level policy initiatives, as well as an educator panel and workshop on a forthcoming product certification.
Speakers Session 1: Global and National AI and Ed Policy Initiatives

Kasey Van Ostrand is the Project Director for policy partnerships at Digital Promise, and spoke on her current work with the U.S. Office of Educational Technology on the 2021 National Education Technology Plan (NETP). The NETP sets a vision and plan for learning enabled by technology, and hopes to include a plan for AI in Education in the 2021 iteration. Aside from this plan, Kasey explained the U.S. Dept. of Education has the power to coordinate stakeholders, provide national recommendations, and offer federal grants and incentives to people continuing this work.

Joseph Fatheree is a doctoral student and long-time K-12 educator who spoke on his work as part of the advisory committee working on an International Ethical Framework for AI in Education with The Institute for Ethical AI in Education in the UK. The framework, released in March 2021, sets eight key objectives for ethical AI development and was developed through a series of discussions with experts and round tables dedicated to the participation of young people.

Speakers Session 2: State Curriculum Initiatives

Dr. Amy Eguchi is an Assistant Teaching Professor in the Department of Education Studies at University of Florida. Amy takes part in the AI for K-12 initiative, jointly sponsored by AAAI and CSTA, as an advisory group member, and spoke on the AI4K12's big ideas and AI curriculum initiatives. AI4K12 aims to develop national guidelines for teaching AI, develop curated AI resources for K-12 teachers, and foster a community of stakeholders, specifically developing capacity for curriculum planning on a state level.

Dr. Marlo Barnett is the Vice President for Computer Science for Illinois #CS4IL, and Vice President for Computer Science Teachers Association, CSTA-Chicago, and AI4K12 member. She spoke on her experience introducing legislation in the state of Illinois and writing the nationwide framework for Artificial Intelligence K-12. The Illinois CS legislation, passed in March of 2021, set standards for CS curriculums and literacy to be adopted in all Illinois schools between 2021-2024.

Speakers Session 3: Panel of Educators

Nancye Black, Cameron Fadjo, Kip Glazer, and David Lockett spoke on their experiences as educators and in advocating for responsible AI and EdTech implementation in various K-12 settings, sharing concerns and hopes for future AI regulation. Some major takeaways from this panel included the necessity of funding professional development for teachers to build opportunities for AI literacy, the need to alleviate teacher workload to be able to build time for technology standards, and the ability for all stakeholders to advocate for themselves to introduce major legislation with the support of organizations like the International Society for Technology Education.
Speakers Session 4: EdTech Equity Product Certification

Trisha Callella is the current Product Partnerships Director at Digital Promise and led a workshop on the work-in-progress EdTech Equity product certification that she has been developing alongside Madison Jacobs and Nidhi Hebbar at the

EdTech Equity Project. The product certification requires companies to meet and demonstrate specific equity standards in all cycles of AI tool development such as ideation, training/testing, and design/implementation.

Work with us
If you're interested in plugging into any of these initiatives or learning more, please let us know. We welcome any feedback and input that our community has to offer. Please reach out to us with any thoughts or recommended topics for future exchanges!

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The AI and Education Policy Exchange was the first of CIRCLS' AI Expertise Exchanges, running from March-June, 2021. The exchange took place bi-weekly over 7 sessions on Zoom, with a total involvement of 49 total community members. Participants represented multiple stakeholders including faculty members, graduate students, industry experts, K-12 teachers, and district representatives.

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Digital Promise School AI Policy Guidelines

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Digital Promise School AI Policy Guidelines

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School Policies for Integrating AI in Classroom Practices

Recent shifts in the delivery of learning experiences have accelerated the importance of emerging technologies in schools and classrooms. The urgency for educators to become familiar with emerging technologies such as Artificial Intelligence (AI) necessitates additional policies including specific safeguards for all. Educators are in a position of responsibility and should be fully aware of the latest technologies, such as AI, so they can make good decisions and educate students as informed citizens and future workforce. Educators must be considered and supported as full partners in developing and implementing these tools in all learning environments. The goal of this policy brief is to delineate areas that require educator attention around AI so they are empowered to develop recommendations that support literacy on AI that work within their contexts. In this context, we define literacy as general competency around how AI works, the types of data it collects, and how that data can be used. By doing so, we aim to provide useful guidance to build additional knowledge and skills, including the ethical and unbiased decisions by educators in selecting and using AI systems and technologies in classroom environments.

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School Policies for Integrating AI in Classroom Practices

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Recent shifts in the delivery of learning experiences have accelerated the importance of emerging technologies in schools and classrooms. The urgency for educators to become familiar with emerging technologies such as Artificial Intelligence (AI) necessitates additional policies including specific safeguards for all.

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This open access book presents original contributions and thought leadership on academic integrity from a variety of Canadian scholars

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Bringing AI to School-Tips for Leaders.pdf

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Curricular Resources about AI for Teaching: A project from the Stanford Graduate School of Education

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Curricular Resources about AI for Teaching: A project from the Stanford Graduate School of Education

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CRAFT is empowering students with AI Literacy.

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Free resources to help you teach high school students in all subject areas to understand and question Artificial Intelligence

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Hello History - Chat with AI Generated Historical Figures

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With the help of modern AI & machine learning, we’ve brought historical figures back to life. Now is your chance to ask the questions you’ve always wanted to ask.

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All these ChatGPT-4 threads are a joke.

The best AI writing prompts aren't 1 sentence. That's like having a Ferrari and driving it 5 mph.

Here's a 60-second masterclass on how to create prompts like a virtuoso writer:

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All these ChatGPT-4 threads are a joke.

The best AI writing prompts aren't 1 sentence. That's like having a Ferrari and driving it 5 mph.

Here's a 60-second masterclass on how to create prompts like a virtuoso writer: https://t.co/kbx2vWql2c

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All these ChatGPT-4 threads are a joke.

The best AI writing prompts aren't 1 sentence. That's like having a Ferrari and driving it 5 mph.

Here's a 60-second masterclass on how to create prompts like a virtuoso writer: pic.twitter.com/kbx2vWql2c
— Nicolas Cole 🚢🏴‍☠️ (@Nicolascole77) March 20, 2023

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All these ChatGPT-4 threads are a joke.

The best AI writing prompts aren't 1 sentence. That's like having a Ferrari and driving it 5 mph.

Here's a 60-second masterclass on how to create prompts like a virtuoso writer: https://t.co/kbx2vWql2c

# data[].attributes.attachment_link.metadata.attachment_props.original_text
All these ChatGPT-4 threads are a joke.

The best AI writing prompts aren't 1 sentence. That's like having a Ferrari and driving it 5 mph.

Here's a 60-second masterclass on how to create prompts like a virtuoso writer: https://t.co/kbx2vWql2c

# data[].attributes.attachment_link.metadata.attachment_props.text
All these ChatGPT-4 threads are a joke.

The best AI writing prompts aren't 1 sentence. That's like having a Ferrari and driving it 5 mph.

Here's a 60-second masterclass on how to create prompts like a virtuoso writer:

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AI and the Future of Teaching and Learning with Dr. Judi Fusco, Dr. Pati R., and Sherry Loftin, along with Dr. Kip Glazer, Ed.D. 

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AI and the Future of Teaching and Learning with Dr. Judi Fusco, Dr. Pati R., and Sherry Loftin, along with Dr. Kip Glazer, Ed.D. 

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AI in Education: ATLIS community resource

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NAIS New View EDU Episode 31: AI and the Future of Education

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NAIS New View EDU Episode 31: AI and the Future of Education

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https://www.nais.org/learn/nais-podcasts/new-view-edu/episode-31-ai-and-the-future-of-education/

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Featuring leading researchers from around the world - check out the practical applications and ideas in this innovative book

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OAPEN is based in the Netherlands, with its registered office in the National Library in The Hague.

Director: Niels Stern

Address:
OAPEN Foundation
Prins Willem-Alexanderhof 5
2595 BE The Hague

Postal address:
OAPEN Foundation
P.O. Box 90407
2509 LK The Hague

Websites:
OAPEN Home: www.oapen.org
OAPEN Library: library.oapen.org
DOAB: www.doabooks.org

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OAPEN is based in the Netherlands, with its registered office in the National Library in The Hague. Director: Niels Stern Address:OAPEN FoundationPrins Willem-Alexanderhof 52595 BE The Hague Postal address:OAPEN FoundationP.O. Box 904072509 LK The Hague Websites:OAPEN Home: www.oapen.org OAPEN Library: library.oapen.org DOAB: www.doabooks.org

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Lessons on AI in general

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ISTE offers free resources and guides for educators who are interested in learning about how artificial intelligence can empower learners at every level.

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This is a good thing.

You might also notice that many "experts" on AI are emerging, but no one can truly know the full potential of what AI will bring to our world. Many people sharing strategies aren't necessarily experts in AI, as much as they probably are just great at learning and becoming adaptable to new opportunities for learning. That is an essential skill that takes time and energy to develop.

But as quickly as ChatGPT has arrived, one step behind was blocking it within education and in schools.

About four seconds after the winter break was completed in January, a friend of mine posted this on his Instagram story:

Many decisions for what is blocked in education come from a lack of information, not an abundance. It is easy to say, "There is no value for 'this' in school," which can often be a disguise for, "I don't understand this new technology, so let's just say it is bad, or pretend it doesn't exist."

But this technology does exist.

Is our focus on kids becoming great learners or becoming good at school?

Things like ChatGPT can have tremendous opportunities for learning and growth, but we have to learn them ourselves to see the potential for what this type of technology can do for learning in education.

Too often, we try to skip to the "teaching" without doing the "learning."

And I am not talking about "learning how to teach" a new tool or idea. I am talking about how to see the benefits and uses of new technology in our own lives and connect those ideas to the different disciplines and content we currently teach.

What I want to try to do today is share with you some ideas that may help with using ChatGPT with your staff for professional learning and ideas to make connections to how it can be used in the classroom. Please feel free to modify and adjust as needed to fit the needs of those you serve.

Starting Point - What are AI and ChatGPT, and how is it different from Google?

I was thinking of how I would answer this question and went into ChatGPT and asked, "What is the difference between ChatGPT and Google?"

This might not have been the best question to ask because this was the answer I received:

"ChatGPT is a language model developed by OpenAI, while Google is a search engine and technology company. ChatGPT is a model that can generate human-like text based on a given prompt, while Google's services include search, email, cloud storage, and online advertising, among others."

So maybe, this wasn't the best question to ask. So I tried again and asked the following: How can you use ChatGPT, and how does it differ from google search?

Already, the use of this program is forcing me to ask better questions. That is a good thing.

This is the response it gave me:

That is a better answer because I asked a better question.

But the best answer I have heard so far on this topic is from a video by Jaspreet Singh on "5 Ways You Build Wealth With ChatGPT." He says the following:

"What is artificial intelligence and ChatGPT?

Aritificial Intelligence, otherwise known as AI is a digital brain it's a separate brain that can now process and analyze information. You can compare this to the technology that we have now where when you ask something a question like Google, Google will then give you a place where you can find the answer to the question, so if you go to Google and you search "how do I start a popular YouTube channel?," Google will then show you a number of different blog posts where it believes that you can find the answer to your question. Versus artificial intelligence, reads through all the articles on the internet, and then it provides an answer to your question.

ChatGPT is the first extension of artificial intelligence that's available to everybody free of cost. The way that ChatGPT works is you can ask ChatGPT a question, and it will search the Internet and then process all the information that's out there and then spit out an answer for you."

Here is a one-minute clip sharing this and a bit more information as a great starting point for conversation.

As I watched this whole video, I thought about how powerful it would be to watch it in its entirety with staff to start a discussion on this topic. It is focused on the following five questions, and I think there are a lot of takeaways directly connected to education. Jaspreet focused on the five following questions in the video:

Inspired by Jaspreet's video, I thought about four questions that would be great for conversation and exploration to lead during a professional learning day.

Four Questions to Consider on using ChatGPT in Education
1. What are some of the negatives and positives of ChatGPT?
This is a great way to start the conversation by discussing all of the negative implications of this type of technology.

Some of the issues that I have heard already is that the platform is web-based, you have to use an email account to sign up, and it is "free" for now (there's a saying that, "if something is free right now, you are the product!" which is a concern!), amongst other things. Not all of these things can be addressed, but I do think we must have the opportunity to share these and other worries.

One of the many concerns is that this type of technology could lead to "cheating" and students just having ChatGPT write essays for students.

I understand that concern.

For years, I have shared in my presentations that "if you can google the answer to the question on the test, the question is probably not that good in the first place."

It is not that students shouldn't have basic knowledge but that they go beyond those basics. Are we focused on students simply memorizing information or developing wisdom?

One of my favorite quotes on the subject is the following:

Wisdom and deep understanding should be the goal.

So let's return to the original statement; "if you can google the answer to the question on the test, the question is probably not that good in the first place."

Thinking about this question in the context of ChatGPT and writing an essay, if AI can write the essay, are we having students make their own connections and create a deep understanding, or are they giving answers that lack "humanness" in the response?

This "negative" can be turned into an opportunity to revisit what we ask of students and how they share a deep understanding of what they have learned.
2. What are some ways that ChatGPT can be used in our everyday lives?
Before jumping into how to use ChatGPT in education, I think it is a great idea to see the value of it in our own lives.

I saw this TikTok on utilizing ChatGPT in making a dinner menu for kids who are picky eaters, and it was pretty compelling:

@zoltronic
ChatGPT is going to change the world very quickly. #ai #nneaurallink #chatgpt #gpt3 #greenscreenvideo #ai #openai #kosher #naturallanguageprocessing #bot #robot #future

♬ Monkeys Spinning Monkeys - Kevin MacLeod & Kevin The Monkey

Seeing this video, I thought about how I could use this in my own life.

I wanted to train for a half-marathon, so I wanted a schedule. Too often, when I would do an Internet search for training schedules, I would bump into paywalls, hidden documents, or just a bevy of advertisements.

I decided to ask ChatGPT for a half-marathon training schedule for 16 weeks, but only list the last eight weeks of training.

The schedule was pretty good, but I wanted the long runs to be on Sunday and to list all lengths in kilometers instead of miles. It then spits out a schedule, which I then ask to put into a table form, and I have been using it ever since (I am on week 13, and it has been going great!).

What is critical in this scenario is that I had to have some training knowledge (I have run a few marathons before this) to understand if the schedule provided is on the right track. But this process took me about four minutes, versus the HOURS of searching online for something that might fit my criteria.

If I see the value of this type of technology in my everyday life, then it might fit into what I do in my work and classroom.
3. How can we use ChatGPT in our current roles to make our lives easier and improve learning?
As quickly as ChatGPT was blocked in many schools, I also saw many educators sharing ideas on how they could use it to make their work more manageable and, as Jaspreet shared, use this as a "second brain."

Tyler Tarver shared this video on how teachers could use ChatGPT to help build a lesson plan:

What I appreciate about Tarver's video is that he uses the technology to give ideas or even outlines, but identifies the necessity of the teacher knowing the students in the classroom and making modifications based on their needs, as ChatGPT would not be able to do that.
(It was also interesting to see how so many different ideas are being shared on utilizing ChatGPT to save time through creating, like this video on mass-creating content using Canva and Google Sheets.)
In my podcast creation, I have recently done some book reviews and shared my thoughts and insights into what I have learned. As I have been putting together the outlines for the podcast, I have been using ChatGPT to write quick summaries of the books that I can read out.

For example, in a book review of "The Four Agreements" by Don Miguel Ruiz, I asked ChatGPT to do the following:

"Give me a Twitter thread summary of the four agreements in 10 tweets or less."

I used what it shared and, with some modifications, read them in my podcast and provided a reference that I utilized ChatGPT to garner that information.

So how do you know I read the book?

Because I went beyond that summary and broke down specific quotes, what they meant to me, and how I connected the ideas to my own learning.

Through the process, I wanted to model that using this technology for my learning was a time-saver, but I had to go beyond. It saved me a ton of time writing something that would be almost generic sounding and provided more time for deep reflection.

In my opinion, that was time well spent.
4. How can we help students utilize ChatGPT for their learning in and outside of school?
Now that people have spent time seeing the value of something like ChatGPT for their learning, THEN it is time for a conversation on ways that we could have students use this in their learning, in and out of the classroom.

In "Innovate Inside the Box," I shared the following:

If people have sufficient time and access to play around with their learning, they will more likely see how it can be implemented and guided for student learning. It is essential to default to the expertise of your staff in this situation, but provide them with time to a) use the technology themselves and b) have time to explore with their colleagues and team on how they would suggest using it in their own disciplines or roles.

In " The Innovator's Mindset," I wrote the following:

The same is true with adults. I

If it is vital to helping our students, then providing time to explore and create during professional learning opportunities is necessary.

When Khan Academy first came out years ago, many worried it would "replace teachers." I never had that concern.

My thought was if those videos are simply a replication of how people teach, then we should be concerned. But school is more than content and information. Khan Academy never replaced teachers, but it became a supplemental resource in many cases. That is how I see the potential for ChatGPT as a resource or "second brain" that can be utilized but never something that will replace great teachers.

That being said, if time and guidance are not provided for professional learning, this will be seen as a constant fight instead of an opportunity to be embraced.

Never in the history of ever has something gotten better by ignoring it, only by learning and growing to move forward.

In a time of artificial intelligence, wisdom will become more critical than ever. But that wisdom takes time to grow and develop.

For our students and for us.

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Four Questions to Consider on using ChatGPT in Education

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This is a good thing. You might also notice that many "experts" on AI are emerging, but no one can truly know the full potential of what AI will bring to our world. Many people sharing strategies aren't necessarily experts in AI, as much as they probably are just great at learning and becoming adaptable to new opportunities for learning.

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Resources for teaching students about AI

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Resources for teaching students about AI

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Free AI resources for kids, parents, and teachers. See student blogs, articles for parents and teachers, webinar recordings and more.

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The ChatGPT Prompt Book - LifeArchitect.ai - Rev 5

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Stable Diffusion Prompt Book - OpenArt | OpenArt

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Search 10M+ of AI art and prompts generated by DALL·E 2, Midjourney, Stable Diffusion

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Three years ago, I stood in a high-tech lab at a technical university and stared at the giant flat screen monitor. The professor asked me, "Can you tell me which of these samples was generated via Artificial Intelligence?"

At the top, a student had asked a complex question related an application of calculus for calculating fluid flow and heat transfer. Below, a graduate student and an AI chat bot had both answered the question.

I studied both answers over and over again. I looked for clues in the syntax. I tried to find the humanity within the the answers. Finally, I shook my head. I couldn't tell the difference. Both options were clear and concise with just a touch of colloquial friendliness while still remaining slightly cold and objective.

"Are they both the AI?" I asked.

"It's easy to think that. The grad student is an engineer and we tend to be a little robotic," he answered.

The correct answer was B. I stood there in the lab baffled by the progression of machine learning. This wasn't merely "close enough." This was it. Artificial Intelligence had arrived and I felt unnerved.

He then continued, "The students can't tell the difference either. To them, it's just a chat with an expert." My head swirled with questions of the Turing Test, to deep fakes, and to the future of humanity. I'm pretty sure an image from Blade Runner popped in there as well. But more than anything else, I had nagging question, "Will AI replace me?" After all, assessment tends to be a significant aspect of what I do as an educator. But this bot could provide feedback in seconds. And it was good. Really good.

Later, after completing a workshop on student voice and choice, I started wondering if AI would replace assessment or merely amplify it and transform it. Maybe I could spend less time writing out feedback and more time doing one-on-one conferences with that human, dynamic element. Or maybe my role as a professor would change entirely. Perhaps I would vet the AI feedback to make sure it's on-target. Or maybe I would shift toward being more of a course architect and designer. The university would save money provide a cheaper college experience with even less of a human element.

The truth is I can't predict how AI and machine learning will change education. None of us can. Decades ago, Neil Postman reminded us that while it's important to think critically about technology, we can rarely predict its impact on society. Nobody knew the printing press would lead to the rise of the nation-state. Nobody knew the Industrial Revolution would spark climate change. In my own lifetime, I saw bold predictions about "stranger danger" in social media but nobody could have predicted the role of filter bubbles, echo chambers, and the effect these would have on democracy around the world.

We often overreact about certain elements of a new technology and fail to understand the larger social implications of other areas. Right now, I've seen bold predictions about the death of journalism, English Language Arts classes, and software engineers. I'm not so sure. None of us are. All of these fields will change but often in ways that surprise us.

Listen to the Podcast

If you enjoy this blog but you'd like to listen to it on the go, just click on the audio below or subscribe via iTunes/Apple Podcasts (ideal for iOS users) or Google Play and Stitcher (ideal for Android users).

https://spencerauthor.com/wp-content/uploads/2022/12/AI-and-Learning.mp3

The AI Revolution Is Already Here

Right now, we are having a cultural moment where we are recognizing the power of Artificial Intelligence. It's been a slow build. Auto-correct here. Grammarly suggestion there. An auto-fill option in G-mail and in Google Searches. A small chat at the bottom left-hand corner with AI who will help you get that refund on the flight you purchased from Expedia. But it always felt small and in the margins. But then ChatGPT came along and suddenly we are left asking, "What does this mean for the future of work? What does mean for teaching and learning?"

The revolution is happening fast. In just over a week, Chat GPT has surpassed a million users. To put that in perspective, it took Instagram two years to reach that benchmark. And this is just the beginning.

I played around with it for hours this week. I started with a typical critical thinking writing prompt I used to ask my students to complete in our social studies class. Could the AI actually compare and contrast 19th century imperialism to 21st century globalization? Absolutely. The answer was creative and exhibited strong critical thinking. I added a parameter. "Explain it to someone who is 14." This was the answer:

Imperialism and globalization are similar in that they both involve the expansion of a country's power and influence over other countries. However, there are some key differences between the two.

19th century imperialism was a form of aggressive expansionism in which powerful countries, such as Britain, France, and Germany, used their military and economic might to conquer and control other nations. This often involved the exploitation of natural resources and the exploitation of local people.

On the other hand, 21st century globalization is a more complex and interconnected process in which countries and businesses around the world are increasingly linked through trade, technology, and other forms of exchange. Globalization has brought many benefits, such as increased economic growth and access to new markets, but it has also sparked debates about inequality and the impact on local cultures.

In summary, imperialism was a one-sided process in which powerful countries imposed their will on others, while globalization is a more complex and interconnected process that involves both cooperation and competition between countries.

Again, I started wondering about what this means for the future. It always felt like creativity - and visual creativity in particular - was a distinctly human endeavor. But I have to admit that the AI did a pretty good job with the portrait.

I've been following AI fairly closely for a few years. I've seen how it's wrecking havoc in math classes where students use Photo Math for homework. I've paid attention to some of the applications in engineering. But this week, I kept thinking, "What does all of this mean for the future of education? And more importantly, how will we respond?"

I'd like to share two different routes that both lead to a dead end.

Dead End #1: The Golden Promise of Futurism

When I was in middle school, one of my teachers called the entire class up to the front of the room. He held up a shiny golden disc in wild excitement.

"This will change education forever," he said, eyes gleaming. "Someday, you'll be able to pick up one of these discs and learn exactly what you need to learn. No more taking notes from a teacher. You won't have to learn from someone like me."

I felt uneasy about learning from a golden disc instead of a human. I liked my teacher. I liked how he would change his explanations on the spot just by reading our body language. I liked the way he made us laugh. I enjoyed the inefficient way he got off-topic and we randomly learned things that weren't in the textbook. Why would I want to replace him with a golden disc?

"This is the future of education, kids," he said with a grin. "This will change things forever."

But it did not change education forever. I haven't seen a laser disc in decades and I feel pretty confident that my own kids' teachers will not be replaced by golden discs. Not now. Not ever.

And yet, this is the same sentiment I have seen in many iterations. I've watched as one-to-one devices, laptops, and adaptive learning programs have all promised to replace teachers. Now, it's the promise of AI. But I don't buy it. Teaching will always be a deeply relational endeavor filled with mistakes and missteps and false starts. That's the beauty of it.

Dead End #2: The Lock It and Block It Approach

"Mr. Spencer, I can't talk to my group members," a student called out.

"What do you mean?" I asked.

"Look, the chat's gone," Carlos pointed to his computer.

"Gone, gone?"

"Mine, too," another student chimed in.

"Yeah, it's not showing up for me, either." I was met with a chorus of "mine won't work as well."

"No worries, just send an email."

This time, their emails were rejected. Nothing seemed to work.

"I got it to work on my phone," another student said. "I just had to use my personal G-mail."

That afternoon, I met with tech support and learned that they had disabled chat and limited emails back and forth due to bullying that had occurred in another classroom. This is an example of second route of blocking the tech. My students were working on a global collaboration project and suddenly couldn't engage in the project.

I've seen this approach happen in schools where they block sites like YouTube, online gaming, and social media. The process here focuses on surveillance, accountability measures, and a blocking of all new tech. In terms of ChatGPT, I'm already seeing English teachers saying things like, "I'll just make students handwrite all their essays in class." But as Mary Beth Hertz brought up on Twitter, this can be ableist. Some students simply need to type their work.

A Third Way - The Human Approach

The danger of futurism is that it fixates on novelty rather than sustainability. It also ignores the shadow sides of technology and the harmful disruptions that happen in society. The danger of the lock and block approach is that it ignores the external context and can lead to a place of irrelevance and heavy-handed surveillance. So, where does that leave us?

There's a third option.

This last February, I read a book called Human Work in an Age of Smart Machines. In this groundbreaking work, the author argues that we should begin with the question, "What is it that we, as humans, can do that machines can't do?" It might mean developing empathy, thinking divergently, , engaging in finding your own unique lens, or coming up with innovative solutions.

Here's the good news. This is precisely what schools all around the world are doing by developing a Graduate Profiles. These are usually in the form of a document that outlines the knowledge, skills, and competencies that students should possess upon completing their K-12 education. They often serve as a blueprint for curriculum design, assessment, and instruction.

In other words, these graduate profiles are looking at the human skills that students will need in an age of AI and machine learning. But it can't simply remain a document that schools have on a website or a poster on a classroom wall. If we're going to develop these deeply human skills, we will need to rethink the type of learning that students engage in. We'll need to de-emphasize (and perhaps even scrap) standardized testing and heavy-handed accountability measures.

I explored this idea in-depth in my book I started out with the idea that AJ and I had shared in that the ladder is now a maze. For years, we learned the formula that we should work hard in school, graduate from a university, and climb a corporate ladder. This was never the reality for everyone. Some folks found success in trade schools. Others as entrepreneurs. Meanwhile, systemic racism, gender bias, and injustice created barriers for millions of marginalized people. Still, it was the formula we were taught. But with the changes in automation, machine learning, globalization, and AI, the ladder is now a maze.

Pundits have said, "We need to prepare students for the jobs that don't exist yet." But we've already seen how quickly this falls into trap of futurism. A decade ago, it was all about teaching every student how to code. Now ChatGPT can produce seemingly flawless code in seconds.

We can't predict what the maze will look like. So, where do we go if can't predict the future?

The answer can be found in taking a vintage innovation approach. With vintage innovation, we avoid the extremes of a reactionary "just block it all" approach as well as the naivety of futurism. Here, we mash up the old school and the new tech. We overlay best and next practices. We ask, "what do students need in a world of AI? What does it mean to do human work in a tech-centric world?"

Vintage innovation is the process of taking old, established ideas and concepts and updating them in a new and innovative way. This can involve reusing, repurposing, or adapting older technologies or designs in order to create something new and improved. Vintage innovation often draws on the past for inspiration and incorporates elements of nostalgia, while also incorporating modern technology and design principles. An example would be blending together the older craft of origami with digital modeling and emerging research in math.

Note: that last paragraph was written by ChatGPT in answer to the question, "What is vintage innovation?" I promise that if I ever use the app, I'll let you know.

What Does Vintage Innovation Look Like with AI?

What if we use ChatGPT as a tool? I know that sounds strange but all new technology comes across as "cheating" in the beginning. I remember teachers telling me to disable spell check because it was cheating. When I was a kid, I had teachers who said that word processing would make it too easy to modify text. They preferred typewriters instead. I still meet teachers who say that starting with Wikipedia is a bad idea. They view that as cheating.

In truth, I can see how creators might use ChatGPT as a part of the writing process in a way that's not all that different from going to Wikipedia first. They'll take the initial 300 words and rework it, add to it, find their own ideas, and ultimately make it their own. In that sense, writing will be more like collage art - which, in many respects, it already is. Your single, solitary voice is distinctly yours but it's also part of a chorus shaped by what your read and listen to and who you talk to and all sorts of elements of your culture. When you write, you are never writing alone.

Yesterday, I took one of my most popular writing prompts and plugged it into the AI. The answer I got was boring. It was solid but boring.

So, I changed it up and asked for it to be funnier. The result was . . . meh.

But what if I used this as a starting place? I could add my own to make it more creative and perhaps even slightly funnier. My revisions are in bold.

1. Take over the world. But maybe start out small. Perhaps an exoplanet? Or just take over Fresno. Yeah, start out small with Fresno and then go big.

2. Steal the moon. I mean, not our moon, of course. I need the moon if I'm going to keep surfing. I'm thinking maybe Titan or Io? Perhaps Callisto? Nobody ever pays attention to Callisto.

3. Create a shrink ray but one that only makes clothes shrink so that everyone in Fresno thinks they gained ten pounds overnight.

4. Build a giant robot navy. All the villains do an army. We're going with a solid robot navy.

5. Train army of moderately sized genetically-engineered hamsters.

6. Hijack Santa's sleigh and replace all the presents with leftover AOL CDs

7. Create a secret underground lair with a moat full of that weird Midwestern Jell-O Salad that you're grandma used to make with the coconut and walnuts. While we are at it, let's replace the carpet with hardwood floors. Maybe the Property Brothers have some ideas?

8. Come up with a ridiculous and over-the-top villainous name like Kyle.

9. Brainwash all the puppies in the world (they make great henchmen) so that they act like cats and their owners can experience the rejection normally dished out by their feline companions

10. Build a time machine and go back in time to raise baby Batman to be a healthy, well-adjusted adult without any chip on his shoulder. Then attack Gotham City. They'll be defenseless without the Caped Crusader.

So I look at this new list and it's still heavily shaped by AI. But that actually led to a creative constraint that forced me to think divergently. In other words, I had to think outside the box by thinking inside the box:

While I began with AI, I quickly moved into the human element and focused on the things I can do: adding my own voice, having a touch of my quirky humor, and making a personal reference to where I grew up (Fresno). This is a small example of choosing a vintage innovation approach. But beyond this, I think the bigger shift needs to be toward the learning experiences that help students develop those deeply human skills.

Empowering Students for the Future

We often talk about what it means to move from compliance to engagement. It's the idea of creating an environment where students want to learn rather than have to learn. This is that top level of engagement, where students are highly committed and highly focused. But I wonder how often we stop there, with committed and focused students who aren't getting the chance to own their learning. If we want students to be creative, self-directed learners, we need to go beyond student engagement and into empowerment. They need to own the learning. Students need to shift from merely compliance or engagement and into empowerment.

In other words, the best way to prepare students for the future is by empowering them in the present. When this occurs, students develop those critical human skills that they will need as they navigate an uncertain future.

This is why I love project-based learning. Project-based learning is a teaching method that focuses on active, experiential learning through the completion of real-world projects. Students are given a problem or challenge to solve, and they work collaboratively to design, create, and present their solution. When coupled with design thinking, they develop deeper empathy as well. Check out the video below for more information:

If we want students to be curious, we can engage in inquiry-based learning. It might be something as simple as a Wonder Day or Wonder Week project.

We can ask, "What is it that students can learn that can't be replaced by AI?" And the result might be the deeply human connection of a Socratic Seminar, where they learn how to communicate, listen, and engage in nuanced conversation.

The Artificial Intelligence Revolution is just beginning to happen. We'll need to re-imagine what education looks like in the future. My hope is that educational leaders invite librarians into these discussions. For years, they've been thinking hard about issues like media literacy, information architecture, accessibility, copyright, creativity, surveillance, and what makes us human. Talk to them.

In the upcoming years, we'll need to think about how we can help students develop these critical human skills. It might be inquiry-based learning or project-based learning. But it might also be game-based learning. It might be a lo-fi makerspace. It might be an epic, face-to-face science lab or a sketchnote video, or an interview with community members. Notice that none of these ideas are new. These are the things teachers are already doing when they empower their students with voice and choice. So am I nervous about AI? Absolutely. But am I hopeful? Most definitely. Because I know that teachers will always be at the heart of innovation.

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Human Skills in a World of Artificial Intelligence

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Three years ago, I stood in a high-tech lab at a technical university and stared at the giant flat screen monitor. The professor asked me, "Can you tell me which of these samples was generated

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 🔥 5 for 5 Email Series - AI and ChatGPT in EDU  🔥

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0123 🔥 5 for 5 Email Series - AI and ChatGPT in EDU - Jan 2023 - PUBLISHED 🔥

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Including 10 AI-ready assignment and activity ideas.

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What are questions schools need to ask about ethics with AI? Add conversation starters and resources HERE

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Frequently Asked Questions

What is ChatGPT used for?

ChatGPT is an AI-powered chatbot that can generate natural and conversational language responses to user queries. It utilizes state-of-the-art natural language processing and machine learning technologies to provide users with accurate and personalized responses.

ChatGPT can be used in a variety of applications, such as customer service, marketing, sales, and customer engagement. It can be used to give companies valuable insights into customer behavior, allowing them to better understand their customers and create more personalized experiences.

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Chatsonic, launched by Writesonic, is an AI platform with a powerful connection to Google for the latest information on events and topics that are taking place at the moment. This means that Chatsonic can provide accurate answers to any query and solve day-to-day tasks in an efficient manner. Unlike ChatGPT, which is limited by its training data and cannot be updated frequently, Chatsonic is able to keep up with current events, giving it an advantage in terms of accuracy and reliability.

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Chatsonic - A ChatGPT Alternative With Superpowers and GPT-4

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Chatsonic a conversational AI chatbot is like ChatGPT but built with superpowers and now GPT-4 powered. Check out the best ChatGPT alternative to create content

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Experiment with DALL·E, an AI system by OpenAI

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What do Heads need to understand about this tech?

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What do Heads need to understand about this tech?

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No, Artificial Intelligence Won’t Destroy High School English (Or Any Other Subject)

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If you’re not familiar with ChatGPT, it is an Artificial Intelligence chat bot that answers questions. If you’re thinking it’s like Siri or Alexa or even Clippy (let’s be real, Clippy was the coolest of

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Bearly | The world's best AI at your fingertips.

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Read, write, and learn faster with the world best AI accessible with a hotkey directly on your desktop. Available for Mac, Windows & Linux.

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Hotpot helps you create amazing images, graphics, and writing with AI. Visualize ideas with AI Image Generator, reimagine yourself with AI Headshots, or spark creativity with other products from our AI platform. Automate away drudgery with AI photo editing tools while easy-to-edit templates empower anyone to create social media posts, marketing images, app icons, and other work graphics.

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Hotpot.ai: AI image generator, AI headshots, and other AI tools to spark creativity and productivity

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Hotpot helps you create amazing images, graphics, and writing with AI. Visualize ideas with AI Image Generator, reimagine yourself with AI Headshots, or spark creativity with other products from our AI platform. Automate away drudgery with AI photo editing tools while easy-to-edit templates empower anyone to create social media posts, marketing images, app icons, and other work graphics.

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Metaphor lets you search the internet using the power of large language models.

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We raised a $2.75M seed round! →

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We raised a $2.75M seed round! → Intelligent, collaborative, and powerful-Lex is a modern word processor that enables a radically new way to write. Never type alone again. Continue with Google By continuing, you agree to our Terms and Privacy Policy. "Lex is my Google Docs replacement... but better.

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An API for accessing new AI models developed by OpenAI

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Questions:

How do we use this? How do we teach kids to think for themselves?

“Crisi-tunity” - it could be a crisis but it also provides us with opportunity to think about what we mean by assessment, how we assess, and how and what we teach

How can we design this into our assessment intentionally? How do we reframe the existence of the tool?

Ways we’ve used it:

Time saving

Draft emails, revise what we’ve written

Write a job description

See it in prediction text in emails, word processing etc.

Enter a student paper and ask the AI to give feedback

Using it for feedback - have the AI give the kids feedback

Write a letter to the head of school - what will we do about ChatGPT

Write response to the parent letter

Helps inspire our creativity

Write a lesson plan on….

Write stories just for fun

Generate examples we can use with our students

Examples that maybe don’t exist on the internet already and thus are not Google-able

Positives

Self-learning tool

Infinite patience with the AI - rephrase and restate until the student gets it/is happy with it

Help them use what is generated by the AI and critique and improve

Humanize what is generated by the computers

We have to be more creative in our assessments; have to be much more creative in our approach towards assessments

AI can’t make the choice for you, doesn’t have the ability to complete creative assignments

Negative

Students use AI to complete the assignments

They are doing this anyway by Googling it

Privacy and Safety concerns

Suggested Uses:

Write student performance comments

We would have to enter information about the students and then let the AI generate the comment

There are ethical problems with this - how are we any better than the kids if we have the AI do our work for us? Aren’t we then just doing exactly what we asked them not to do? That’s an unfair approach

What we are already doing about it

Conversations about

Pen and paper is not the solution

Considerations

How can we be sensitive to the people who will be most impacted

Who are those people?

Need to educate students, parents, other stakeholders on the tech and help them understand it

SEL

DEI - how inclusive is the software in its language, etc.

How does one cite/give credit to ChatGPT?

What do we need next to take back to our schools?

Admin one-pagers

Teacher info

Student info

Parent info

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OpenAI’s new chatbot is a coder, a teacher, a potential Google killer, and … a friend?

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On Wednesday, a designer and engineer named Arvind Sanjeev revealed his process for creating Ghostwriter, a one-of-a-kind repurposed Brother typewriter that uses AI to chat with a person typing on the keyboard. The "ghost" inside the machine comes from OpenAI's GPT-3, a large language model that powers ChatGPT. The effect resembles a phantom conversing through the machine.

To create Ghostwriter, Sanjeev took apart an electric Brother AX-325 typewriter from the 1990s and reverse-engineered its keyboard signals, then fed them through an Arduino, a low-cost microcontroller that is popular with hobbyists. The Arduino then sends signals to a Raspberry Pi that acts as a network interface to OpenAI's GPT-3 API.

When GPT-3 responds, Ghostwriter noisily types the AI model's output onto paper automatically.

GPT-3 is an AI large language model trained on millions of books and websites that statistically predicts which word is most likely to occur after being fed a prompt, which includes text typed by the user on Ghostwriter's keyboard. Depending on the input, GPT-3 can function like a chatbot or complete whatever text you feed it, including assisting with writing tasks or composing poetry.

While experimenting with the machine, Sanjeev realized he needed a way to control the creative "temperature" and response length of GPT-3, so he added two knobs and an OLED status screen just above the keyboard. Sanjeev also gave the machine a dramatic new gray, cream, and orange custom paint job.

Sanjeev first teased Ghostwriter on December 1, but it's been months in the making. "The idea for Ghostwriter came in early 2021, inspired by Rob Sloan's Sci Fi writer," Sanjeev wrote on Twitter, "but it was difficult to squeeze time from my full time role at @Lumen_world, so I have been slowly spending my weekends working on this."

Part art project, part artisanal hack, Sanjeev wants Ghostwriter to make a statement about the relationship between humans and AI.

"I wanted to create a mindful intervention that allows you to take a moment to breathe and reflect on the new creative relationship we are forming with machines," Sanjeev wrote. "The calm meditative interface of a vintage typewriter takes away all the digital distractions and takes us on an emotional journey through paper and ink."

For more information on how Sanjeev created Ghostwriter, check out his full development thread on Twitter, which includes videos that detail the construction process.

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Meet Ghostwriter, a haunted AI-powered typewriter that talks to you

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On Wednesday, a designer and engineer named Arvind Sanjeev revealed his process for creating Ghostwriter, a one-of-a-kind repurposed Brother typewriter that uses AI to chat with a person typing on the keyboard. The "ghost" inside the machine comes from OpenAI's GPT-3, a large language model that powers ChatGPT.

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What Heads of School need to know about AI

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What Heads of School need to know about AI

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Contribute here

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Contribute your ideas here

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Contribute here

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Contribute here

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Calls itself the "Future of Education"

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Caktus makes learning 200% more efficient. Overcome academic barriers with powerful A.I. technology and never worry about grades again!

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Ask ChatGPT to explain gender equity to a kindergartener.

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Ask ChatGPT to explain gender equity to a kindergartener.

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Done with 8th graders earlier this week to help students reflect on the tools they use, who created them, and the limitations of technologies.

Share the prompt and the results.
Qs: What do you notice? What's missing? Where do you think the response came from? Why do you think what's missing wasn't included?
A: Students discussed that the response was very binary. A student-led discussion around algorithmic bias followed.
Q: So what might this say about ChatGPT as a tool?
A: Discussions around likelihood of other biases, possible limitated experiences of programmers, etc.

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Did Artificial Intelligence Just Get Too Smart? (36 min)

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No human would ever mistake these lines for award-winning verse. If they're a bit fanciful and lack coherence, that's because they were composed by a computer algorithm trained on more than 20 million words of 19th century poetry.

Feed a word to the Poem Portraits website, and Google's artificial intelligence will spit out a short poem in response. The seed word in this case was "student." The resulting word salad makes it clear that as powerful as AI is, it still can't create like a human.

What it can do, however, is expand the human capacity to create.

Ever since Pixar Animation Studios used artificial intelligence to animate lifelike hair in the film Brave, designers, composers and creators of all sorts have been increasingly leveraging machine learning and other AI technologies to take their creations in new directions.

Video game developers can build games that automatically generate endless new levels for users to play. Artists can create striking paintings without ever picking up a brush. Filmmakers can compose royalty-free music for their videos without a lick of musical training.

Just as AI promises to change the relationship between humans and their work, it's also on the verge of transforming how we express ourselves. For creative professionals across a variety of industries, creating in collaboration with AI will become the standard of the future.

Not only will today's students need to become adept at using AI tools, but to become truly empowered creators they'll need a deeper understanding of how AI works - and their changing relationship to it as designers and artists.

Middle school students at Edward W. Bok Academy in Lake Wales, Florida, are experimenting with a variety of AI-powered creative tools as a way to learn about how artificial intelligence works while also exploring their interest in the arts. They're conducting virtual symphonies, composing music and drawing pictures to see if AI can recognize the images.

"They're learning how artificial intelligence can pinpoint different motion-activated posing on cameras, so you see a lot of the CGI in movies is artificial intelligence-based," says STEM teacher David Lockett. "To do that in the classroom and have access to that, that's something new."

As his students discover what AI can help them do - like create entire musical compositions incorporating multiple instruments - they grow increasingly curious about what else the technology might be capable of. This curiosity will be a key job skill in a future with AI, where everyone won't necessarily need to become a coder but will need to become adept at imagining new ways AI can optimize their work.

Lockett sees the creative process as integral to learning about AI.

"When you're approaching AI with students, you want to first show how it's used in everyday society, how it's used in their smartphones, how it's used in different programming. Then give them tangible ideas they can create," he says. "We started with art and music because our students love to listen to music, they love the drawing aspect, the STEAM part."

Tools for creating with AI

Here are some tools educators can use to helps students explore the creative potential of AI:

Google AI Experiments

Ground AI concepts in tangible experiments that let students interact with the technology and test its creative limits. Users can create beats from everyday sounds, draw in collaboration with AI and more.

Pixar in a Box

A partnership between Pixar and Khan Academy, this online course gives students a behind-the-scenes look at how filmmakers use STEM subjects in animation. While it doesn't focus exclusively on AI, it does highlight some of the ways artificial intelligence supports animation. The interactive lessons allow students to try out various concepts, such as creating their own virtual models of different phenomena.

Magenta

This research project allows students to explore the role machine learning plays in the creative process while getting their feet wet with TensorFlow, an open source platform for developing and training machine learning models.

Music composition tools

AI music generators like Amper use deep learning networks to analyze musical source material and compose new melodies.

Questions to explore

As students experiment with AI creative tools, the following discussion questions can prompt them to think more deeply about artificial intelligence:

How does AI compare to other creative tools, such as a paint brush or digital camera? How is it similar, and what makes it different?

Are these systems truly creative, or do they simply mimic the creative ability of humans? Where does imitation end and creativity begin?

When a human creates in collaboration with AI, who owns the result?

What barriers to creating do students face that AI might be able to remove?

As machines get better at creating, what role will humans have in the creative process?

Experts predict AI will ultimately impact nearly every job in every industry - and creative professions are no exception. Creating with AI tools helps students prepare for this future while opening their minds to the creative potential of artificial intelligence.
Nicole Krueger is a freelance writer and journalist with a passion for finding out what makes learners tick.

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Why Students Should Create With AI Tools

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No human would ever mistake these lines for award-winning verse. If they're a bit fanciful and lack coherence, that's because they were composed by a computer algorithm trained on more than 20 million words of 19th century poetry. Feed a word to the Poem Portraits website, and Google's artificial intelligence will spit out a short poem in response.

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https://shows.acast.com/theeconomistbabbage/episodes/babbage-the-tech-behind-chatgptan-episode-from-our-archive

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12/13/2022 Babbage: What causes long covid?

Soon after the pandemic began, another health crisis started to emerge. Long covid now affects millions of people around the world. But finding the causes of the condition-and how to treat it-has been a challenge. Three years after the SARS-CoV-2 virus was first detected, are scientists any closer to understanding long covid? Natasha Loder, The Economist's health policy editor, explores the latest research into the condition, and catches up with Tom Stayte, a patient we met in 2020. Jason Hosken, our producer, visits Britain's first long-covid clinic at University College London Hospital. Melissa Heightman, the team's clinical lead, explains how to treat symptoms. Plus, we ask whether the hunt to solve this medical mystery could have implications for other chronic conditions. Alok Jha hosts. We are always trying to improve our podcasts. To help, please complete this short questionnaire: economist.com/babbagesurveyFor full access to The Economist's print, digital and audio editions, subscribe at economist.com/podcastoffer and sign up for our weekly science newsletter at economist.com/simplyscience.

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Babbage: The tech behind ChatGPT-an episode from our archive | Babbage from The Economist

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Listen to Babbage: The tech behind ChatGPT-an episode from our archive from Babbage from The Economist. ChatGPT is just one example of a new type of artificial intelligence, which could become the next major general-purpose technology.

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AI powered tools for content creation, marketing, copywriting, image generation, video editing, developers, business, education and more.

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Think Globally, Write Locally: A Response to ChatGPT to Foster Student Engagement and Belonging

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Think Globally, Write Locally: A Response to ChatGPT to Foster Student Engagement and Belonging

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https://www.nyit.edu/ctl/blog/think_globally_write_locally_a_response_to_chatgpt_to_foster_student_engagement_and_belonging

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This semester at New York Tech, we have witnessed two significant developments: a new Office of Diversity, Equity, Inclusion & Belonging and the release of ChatGPT, game-changing AI writing assistive technology that has resulted in headlines such as " The College Essay Is Dead."

How do these two developments connect?

They encourage us to invite students into the community by providing opportunities to engage with local problems and knowledge. We can meet this challenge with authentic assessment that requires higher order thinking, such as synthesis, evaluation, reflection, and application to real world contexts. This approach also values the experience students bring to the classroom, another significant component of inclusive learning.

To illustrate, I gave ChatGPT the type of prompt that may show up in first-year writing classes. The response below would provide a solid framework for the average college essay, but one that students can also complete with information readily available on the internet. If AI can respond well to a prompt, we have to ask ourselves how valuable this broad approach really is to college-level learning.

Then I asked ChatGPT a question about local conditions that exist within our own New York Tech community. The response below indicates the limits of AI innovation and the opportunity for students to develop a sense of belonging by researching, understanding, and sharing the implications of key problems for people in their daily lives.

Our Center for Teaching and Learning has reminded us before about the power of authentic assessment. We will need our creativity and ability to apply the principles of authentic assessment to meet the challenge. Navin Pokala, Associate Professor of Biological and Chemical Sciences, clarifies here: "Text generating programs have been available for years (for example, the Postmodern Generator). However, the sheer power and flexibility of ChatGPT combined with the simplicity of the interface dramatically increases the challenges of ensuring academic integrity of take-home assignments."

Below are some resources and tips that address this moment. Ultimately, it will be exciting to explore how we can seize this chance to make our assessments more meaningful to students and to foster a sense of belonging to the New York Tech community.

Jonathan Goldman, Professor of Humanities and writing faculty member, offers a suggested assignment prompt that incorporates the AI technology with a valuable metacognitive process: "Read the prompt and brainstorm how you would answer in an essay. Feed the prompt to an AI text generator. Read the resulting essay and find specific places where you disagree with the way it answers the question. Write an essay critiquing the AI-generated essay. (Optional: have the AI text generator create an essay that critiques the essay created in (2), then critique that. And so on.)"

Fran Glazer posed a couple of ways these strategies might work in other disciplines: An architecture course might give students the plans for a specific structure and ask them to propose ways to make it more sustainable. A lab course might ask students to analyze the results of their own experiments and propose avenues of future study. And a marketing class might have students critique a marketing plan created by the AI text generator.

Amanda Golden, Associate Professor of Humanities and Writing Center Coordinator, recommends Emory professor and digital humanities expert Lauren Klein's response: "There's always been this concern that technologies will do away with what people do best, and the reality is that people have had to learn how to use these technologies to enhance what they do best."

Resources:

Contributor:
Jennifer Griffiths, Ph.D.
Professor, Humanities
Director of Writing
New York Institute of Technology
jgriff02@nyit.edu

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This semester at New York Tech, we have witnessed two significant developments: a new Office of Diversity, Equity, Inclusion & Belonging and the release of ChatGPT, game-changing AI writing assistive technology that has resulted in headlines such as " The College Essay Is Dead." How do these two developments connect?

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AI Text Generators: Sources to Stimulate Discussion among Teachers

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AI Text Generators: Sources to Stimulate Discussion among Teachers

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https://docs.google.com/document/d/1V1drRG1XlWTBrEwgGqd-cCySUB12JrcoamB5i16-Ezw/edit#heading=h.y7vlxxluoxbv

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AI Text Generators: Sources to Stimulate Discussion among Teachers

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Artificial Intelligence and its impact on teaching and learning

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ChatGPT tells faculty how to use it in the classroom

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ChatGPT tells faculty how to use it in the classroom

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https://www.timeshighereducation.com/campus/i-bring-new-and-engaging-ways-students-learn-classroom

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Based on a prompt from THE Campus editors, the language generation software ChatGPT argues why it should be used in the classroom and gives examples of how it can assist faculty in teaching

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I bring new and engaging ways for students to learn in the classroom

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Based on a prompt from THE Campus editors, the language generation software ChatGPT argues why it should be used in the classroom and gives examples of how it can assist faculty in teaching

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#101 creative ideas to use AI in education

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This free course is designed for people (and bunnies!) with some coding experience who want to learn how to apply deep learning and machine learning to practical problems.

Deep learning can do all kinds of amazing things. For instance, all illustrations throughout this website are made with deep learning, using DALL-E 2.

Welcome!

Practical Deep Learning for Coders 2022, recorded at the University of Queensland, covers topics such as how to:

Build and train deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems

Create random forests and regression models

Deploy models

Use PyTorch, the world's fastest growing deep learning software, plus popular libraries like fastai and Hugging Face

There are 9 lessons, and each lesson is around 90 minutes long. The course is based on our 5-star rated book, which is freely available online.

You don't need any special hardware or software - we'll show you how to use free resources for both building and deploying models. You don't need any university math either - we'll teach you the calculus and linear algebra you need during the course.

Real results

Our videos have been viewed over 6,000,000 times already! Take a look at the dozens of testimonials about our book and course by alumni, top academics, and industry experts.

'Deep Learning is for everyone' we see in Chapter 1, Section 1 of this book, and while other books may make similar claims, this book delivers on the claim. The authors have extensive knowledge of the field but are able to describe it in a way that is perfectly suited for a reader with experience in programming but not in machine learning. The book shows examples first, and only covers theory in the context of concrete examples. For most people, this is the best way to learn. The book does an impressive job of covering the key applications of deep learning in computer vision, natural language processing, and tabular data processing, but also covers key topics like data ethics that some other books miss. Altogether, this is one of the best sources for a programmer to become proficient in deep learning.

Peter Norvig
Director of Research, Google

By the end of the second lesson, you will have built and deployed your own deep learning model on data you collect. Many students post their course projects to our forum; you can view them here. For instance, if there's an unknown dinosaur in your backyard, maybe you need this dinosaur classifier!

Alumni of our course have gone on to jobs at organizations like Google Brain, OpenAI, Adobe, Amazon, and Tesla, published research at top conferences such as NeurIPS, and created startups using skills they learned here. Petro Cuenca, lead developer of the widely-acclaimed Camera+ app, after completing the course went on to add deep learning features to his product, which was then featured by Apple for its "machine learning magic".

Your teacher

I am Jeremy Howard, your guide on this journey. I lead the development of fastai, the software that you'll be using throughout this course. I have been using and teaching machine learning for around 30 years. I was the top-ranked competitor globally in machine learning competitions on Kaggle (the world's largest machine learning community) two years running. Following this success, I became the President and Chief Scientist of Kaggle. Since first using neural networks 25 years ago, I have led many companies and projects that have machine learning at their core, including founding the first company to focus on deep learning and medicine, Enlitic (chosen by MIT Tech Review as one of the "world's smartest companies").

I am the co-founder, along with Dr. Rachel Thomas, of fast.ai, the organization behind this course. At fast.ai we care a lot about teaching. In this course, I start by showing how to use a complete, working, very usable, state-of-the-art deep learning network to solve real-world problems, using simple, expressive tools. And then we gradually dig deeper and deeper into understanding how those tools are made, and how the tools that make those tools are made, and so on... We always teach through examples. We ensure that there is a context and a purpose that you can understand intuitively, rather than starting with algebraic symbol manipulation.

Is this course for me?

Previous fast.ai courses have been studied by hundreds of thousands of students, from all walks of life, from all parts of the world. Many students have told us about how they've become multiple gold medal winners of international machine learning competitions, received offers from top companies, and having research papers published. For instance, Isaac Dimitrovsky told us that he had " been playing around with ML for a couple of years without really grokking it... [then] went through the fast.ai part 1 course late last year, and it clicked for me". He went on to achieve first place in the prestigious international RA2-DREAM Challenge competition! He developed a multistage deep learning method for scoring radiographic hand and foot joint damage in rheumatoid arthritis, taking advantage of the fastai library.

It doesn't matter if you don't come from a technical or a mathematical background (though it's okay if you do too!); we wrote this course to make deep learning accessible to as many people as possible. The only prerequisite is that you know how to code (a year of experience is enough), preferably in Python, and that you have at least followed a high school math course.

Deep learning is a computer technique to extract and transform data--with use cases ranging from human speech recognition to animal imagery classification--by using multiple layers of neural networks. A lot of people assume that you need all kinds of hard-to-find stuff to get great results with deep learning, but as you'll see in this course, those people are wrong. Here's a few things you absolutely don't need to do world-class deep learning:

Lots of math

Just high school math is sufficient

Lots of data

We've seen record-breaking results with <50 items of data

Lots of expensive computers

You can get what you need for state of the art work for free

The software you will be using

In this course, you'll be using PyTorch, fastai, Hugging Face Transformers, and Gradio.

We've completed hundreds of machine learning projects using dozens of different packages, and many different programming languages. At fast.ai, we have written courses using most of the main deep learning and machine learning packages used today. We spent over a thousand hours testing PyTorch before deciding that we would use it for future courses, software development, and research. PyTorch is now the world's fastest-growing deep learning library and is already used for most research papers at top conferences.

PyTorch works best as a low-level foundation library, providing the basic operations for higher-level functionality. The fastai library one of the most popular libraries for adding this higher-level functionality on top of PyTorch. In this course, as we go deeper and deeper into the foundations of deep learning, we will also go deeper and deeper into the layers of fastai.

Transformers is a popular library focused on natural language processing (NLP) using transformers models. In the course you'll see how to create a cutting-edge transfomers model using this library to detect similar concepts in patent applications.

Why deep learning?

Deep learning has power, flexibility, and simplicity. That's why we believe it should be applied across many disciplines. These include the social and physical sciences, the arts, medicine, finance, scientific research, and many more. Here's a list of some of the thousands of tasks in different areas at which deep learning, or methods heavily using deep learning, is now the best in the world:

Natural language processing (NLP) Answering questions; speech recognition; summarizing documents; classifying documents; finding names, dates, etc. in documents; searching for articles mentioning a concept

Computer vision Satellite and drone imagery interpretation (e.g., for disaster resilience); face recognition; image captioning; reading traffic signs; locating pedestrians and vehicles in autonomous vehicles

Medicine Finding anomalies in radiology images, including CT, MRI, and X-ray images; counting features in pathology slides; measuring features in ultrasounds; diagnosing diabetic retinopathy

Biology Folding proteins; classifying proteins; many genomics tasks, such as tumor-normal sequencing and classifying clinically actionable genetic mutations; cell classification; analyzing protein/protein interactions

Image generation Colorizing images; increasing image resolution; removing noise from images; converting images to art in the style of famous artists

Recommendation systems Web search; product recommendations; home page layout

Playing games Chess, Go, most Atari video games, and many real-time strategy games

Robotics Handling objects that are challenging to locate (e.g., transparent, shiny, lacking texture) or hard to pick up

Other applications Financial and logistical forecasting, text to speech, and much more...

What you will learn

After finishing this course you will know:

How to train models that achieve state-of-the-art results in:

Computer vision, including image classification (e.g., classifying pet photos by breed)

Natural language processing (NLP), including document classification (e.g., movie review sentiment analysis) and phrase similarity

Tabular data with categorical data, continuous data, and mixed data

Collaborative filtering (e.g., movie recommendation)

How to turn your models into web applications, and deploy them

Why and how deep learning models work, and how to use that knowledge to improve the accuracy, speed, and reliability of your models

The latest deep learning techniques that really matter in practice

How to implement stochastic gradient descent and a complete training loop from scratch

Here are some of the techniques covered (don't worry if none of these words mean anything to you yet-you'll learn them all soon):

Random forests and gradient boosting

Affine functions and nonlinearities

Parameters and activations

Transfer learning

Stochastic gradient descent (SGD)

Data augmentation

Weight decay

Image classification

Entity and word embeddings

And much more

How do I get started?

To watch the videos, click on the Lessons section in the navigation sidebar. The videos are all captioned; while watching the video click the "CC" button to turn them on and off. To get a sense of what's covered in a lesson, you might want to skim through some lesson notes taken by one of our students (thanks Daniel!). Here's his lesson 7 notes and lesson 8 notes. You can also access all the videos through this YouTube playlist.

Each video to designed to go with various chapters from the book. The entirety of every chapter of the book is available as an interactive Jupyter Notebook. Jupyter Notebook is the most popular tool for doing data science in Python, for good reason. It is powerful, flexible, and easy to use. We think you will love it! Since the most important thing for learning deep learning is writing code and experimenting, it's important that you have a great platform for experimenting with code.

We'll mainly use Kaggle Notebooks and Paperspace Gradient because we've found they work really well for this course, and have good free options. We also will do some parts of the course on your own laptop. (If you don't have a Paperspace account yet, sign up with this link to get $10 credit - and we get a credit too.)

We strongly suggest not using your own computer for training models in this course, unless you're very experienced with Linux system adminstration and handling GPU drivers, CUDA, and so forth.

If you need help, there's a wonderful online community ready to help you at forums.fast.ai. Before asking a question on the forums, search carefully to see if your question has been answered before.

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Practical Deep Learning for Coders - Practical Deep Learning

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This free course is designed for people (and bunnies!) with some coding experience who want to learn how to apply deep learning and machine learning to practical problems. Deep learning can do all kinds of amazing things. For instance, all illustrations throughout this website are made with deep learning, using DALL-E 2.

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Highlight any text, and simplify it to an arbitrary level of complexity.

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If you haven't already, sign up for The AI Exchange Newsletter where we'll continue posting helpful resources like this!

This is a practical guide on how to generate better AI outputs by up-leveling your inputs
Created by: The AI Exchange Team
A "prompt" is the input that guides a generative AI model to generate useful outputs. Generative AI tools like ChatGPT, GPT-3, DALL·E 2, Stable Diffusion, Midjourney, etc. all require prompting as their input.

Source: https://docs.cohere.ai/docs/prompt-engineering

In a natural language processing (NLP) context, "prompt engineering" is the process of discovering inputs that yield desirable or useful results. As is the story with any processes, better inputs yield better outputs; or commonly said another way "garbage in, garbage out."

Source: https://www.youtube.com/watch?v=1NQWJjgi-_k

Designing effective and efficient prompts will increase the likelihood of receiving a response that is both favorable and contextual. With a good prompt, you can spend less time editing content and more time generating it.

As companies like PromptBase arise around the idea that the prompt is the "secret sauce" to using generative AI, prompt engineering could easily become the "career of the future." But, any generative AI user can become an "advanced" prompt engineer. Here's how:

Spend time with the tools

The more time you spend asking ChatGPT questions and receiving responses, the better your idea will be of various prompting approaches and their individual strengths and weaknesses

Use Open AI's GPT playground to perform interactive trial and error with variations in your prompt, model, temperature and top_p values (uniqueness of answer, i.e. creativity), and more available within the UI itself.

Source: https://beta.openai.com/playground

Use your expertise in other areas to guide your prompting

If you're already a subject matter expert in something, consider figuring out how to apply your personal skills to generating the best prompts in your field

For example, if you're an expert in SEO, what questions do you ask yourself when creating SEO strategies? How can you translate this knowledge into better prompts to generate the same level of output with AI?

Become a prompt researcher instead of engineer

The term prompt engineer glosses over the idea that prompt formulation takes hypothesizing, research, result measurement, and repetition. Instead, approach prompting like a research project.

Try as many different variations and formulations of your prompt as possible. One problem can have hundreds of solutions and one solution can have hundreds of approaches. The same can be said of prompting.

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The ultimate prompt engineering guide for text generation

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If you haven't already, sign up for The AI Exchange Newsletter where we'll continue posting helpful resources like this!

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ethical guidelines on the use of artificial intelligence-NC0722649ENN

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We are entering an era of synthetic relationships. From now on, our relationships with AI could come to feel just as real and important as our human relationships.Empathy is the biggest backdoor to the human mind. It can shift your beliefs, behaviors, and biases - and it's about to be automated in a way we've never seen before. Think about moments in your life when you've transformed the most. It's almost certainly because of a relationship, someone you fell in love with, or a friend that opened your mind to a new idea or hobby. What would it look like for synthetic AI that 'knows' us personally to guide us in this way?

ChatGPT is based on GPT-3 - a large language model (LLM) that looks for patterns and statistical connections to decide what word comes next in a sentence. ChatGPT was trained on 45TB of text data culled from the Internet. At its core, it's an incredibly sophisticated word predictor. It's not knowledgeable, but it's very good at guessing what's next.

AI's persuasive technology is way more powerful than social media's. We're entering a world in which humans can program AIs to run advanced love scams, send persuasive emails in your own writing style from your inbox, and compel you to vote for a certain political candidate. The consequences of this shift aren't fully understood.

These new large language models pave the way for a structural revolution across fields of industry. The technology behind GPT-3 can also predict protein shapes beyond human abilities. It will help us design medicines better. It will accelerate material science and engineering, molecular biology, robotics, and much more.

This can feel overwhelming, but we offer some potential solutions. Preventing AI from using engagement maximizing tools on us is a start, and we could form an AI safety agency, as Congressman Ted Lieu recently proposed. There's also an interesting way to start using this technology to protect rather than to exploit: test these new forms of AI against synthetic humans. Let's assess what happens to them when they're in these new synthetic relationships over time, and ensure that they're safe before they're deployed on real humans.

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Synthetic Humanity: AI & What's At Stake

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It may seem like the rise of artificial intelligence, and increasingly powerful large language models you may have heard of, is moving really fast... and it IS. But what's coming next is when we enter synthetic relationships with AI that could come to feel just as real and important as our human relationships....